scholarly journals Cadaver Kidney Demand Forecasting and Classification Modelling of Kidney Allocation–A Case Study

2016 ◽  
Vol 25 ◽  
pp. 1162-1169 ◽  
Author(s):  
Pius Tom ◽  
K. Sunil Kumar
Keyword(s):  
Demand Forecasting ◽  
Kidney Allocation ◽  
Cadaver Kidney

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town

scholarly journals Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

2019 ◽  
Vol 1284 ◽  
pp. 012004 ◽  
Author(s):  
Leandro L Lorente-Leyva ◽  
Jairo F Pavón-Valencia ◽  
Yakcleem Montero-Santos ◽  
Israel D Herrera-Granda ◽  
Erick P Herrera-Granda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural

scholarly journals Relief Demand Calculation in Humanitarian Logistics Using Material Classification

2020 ◽  
Vol 17 (2) ◽  
pp. 582
Author(s):  
Jianfang Shao ◽  
Changyong Liang ◽  
Xihui Wang ◽  
Xiang Wang ◽  
Liang Liang
Keyword(s):  
Disaster Response ◽  
Demand Forecasting ◽  
Humanitarian Logistics ◽  
New Method ◽  
On Demand ◽  
Material Classification ◽  
Response Practice ◽  
Different Types ◽  
General Method

Demand calculation, which is the base of most logistics decisions and activities, is a critical work in humanitarian logistics (HL). However, previous studies on demand calculation in HL mainly focus on demand forecasting methodology, with many neglecting the checklist of critical supplies and practice background. This work proposes a new method for relief demand calculation by dividing the process into two parts: supply classification and demand calculation. A general method for classifying relief supplies and clarifying the checklist of relief items for multi-disaster and multiple natural scenarios is given in detail, followed by the procedure of demand calculation for each relief material. The authors present a case study to validate the feasibility and effectiveness of the proposed method based on the disaster response practice in China. Detailed lists of relief demand for different types and severities of disaster are provided.

scholarly journals Intelligent system for non-technical losses management in residential users of the electricity sector

2018 ◽  
Vol 38 (2) ◽  
pp. 52-60 ◽  
Author(s):  
Miguel Uparela Cantillo ◽  
Ruben González ◽  
Jamer Jiménez Mares ◽  
Christian Quintero Monroy
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
Demand Forecasting ◽  
Electricity Sector ◽  
Caribbean Region ◽  
Self Organizing Maps ◽  
Detection Rates ◽  
Distribution Company

The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company.

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